Time-Since-Infection Model for Hospitalization and Incidence Data
arxiv(2024)
摘要
The Time Since Infection (TSI) models, which use disease surveillance data to
model infectious diseases, have become increasingly popular recently due to
their flexibility and capacity to address complex disease control questions.
However, a notable limitation of TSI models is their primary reliance on
incidence data. Even when hospitalization data are available, existing TSI
models have not been crafted to estimate disease transmission or predict
disease-related hospitalizations - metrics crucial for understanding a pandemic
and planning hospital resources. Moreover, their dependence on reported
infection data makes them vulnerable to variations in data quality. In this
study, we advance TSI models by integrating hospitalization data, marking a
significant step forward in modeling with TSI models. Our improvements enable
the estimation of key infectious disease parameters without relying on contact
tracing data, reduce bias in incidence data, and provide a foundation to
connect TSI models with other infectious disease models. We introduce
hospitalization propensity parameters to jointly model incidence and
hospitalization data. We use a composite likelihood function to accommodate
complex data structure and an MCEM algorithm to estimate model parameters. We
apply our method to COVID-19 data to estimate disease transmission, assess risk
factor impacts, and calculate hospitalization propensity.
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